DocumentCode
1745031
Title
A Dempster-Shafer theory of evidence approach for combining trained neural networks
Author
Al-Ani, Ahmed ; Deriche, Mohamed
Author_Institution
Signal Process. Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia
Volume
3
fYear
2001
fDate
6-9 May 2001
Firstpage
703
Abstract
The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since there is not a unique way to perform such a combination, we have developed an algorithm which adapts to the training data set so that the overall mean square error is minimised. The proposed method was proved to be superior and more robust than other available combination methods
Keywords
learning (artificial intelligence); neural nets; pattern classification; Dempster-Shafer theory of evidence; classifiers; combination method; mean square error minimisation; neural network combining; trained neural networks; training data set; Artificial neural networks; Atomic measurements; Australia; Mean square error methods; Neural networks; Pattern recognition; Robustness; Signal processing; Signal processing algorithms; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on
Conference_Location
Sydney, NSW
Print_ISBN
0-7803-6685-9
Type
conf
DOI
10.1109/ISCAS.2001.921429
Filename
921429
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